diff --git a/.blackboxrules b/.blackboxrules
index 6125caa..af4572e 100644
--- a/.blackboxrules
+++ b/.blackboxrules
@@ -23,10 +23,10 @@ A pipeline file has three main sections:
 
 #### Domain Statement
 ```plx
-domain = "domain_name"
+domain = "domain_code"
 description = "Description of the domain" # Optional
 ```
-Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
+Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
 
 #### Concept Definitions
 
@@ -62,7 +62,7 @@ For details on how to structure concepts with fields, see the "Structuring Model
 ### Pipe Base Definition
 
 ```plx
-[pipe.your_pipe_name]
+[pipe.your_pipe_code]
 type = "PipeLLM"
 description = "A description of what your pipe does"
 inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" }
@@ -471,7 +471,7 @@ The PipeExtract operator is used to extract text and images from an image or a P
 [pipe.extract_info]
 type = "PipeExtract"
 description = "extract the information"
-inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input.
+inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input.
 output = "Page"
 ```
 
@@ -480,7 +480,7 @@ Using Extract Model Settings:
 [pipe.extract_with_model]
 type = "PipeExtract"
 description = "Extract with specific model"
-inputs = { document = "PDF" }
+inputs = { document = "Document" }
 output = "Page"
 model = "base_extract_mistral"  # Use predefined extract preset or model alias
 ```
@@ -588,15 +588,16 @@ $sales_rep.phone | $sales_rep.email
 """
 ```
 
-#### Key Parameters
+#### Key Parameters (Template Mode)
 
-- `template`: Inline template string (mutually exclusive with template_name)
+- `template`: Inline template string (mutually exclusive with template_name and construct)
 - `template_name`: Name of a predefined template (mutually exclusive with template)
 - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.)
 - `templating_style`: Styling options for template rendering
 - `extra_context`: Additional context variables for template
 
 For more control, you can use a nested `template` section instead of the `template` field:
+
 - `template.template`: The template string
 - `template.category`: Template type
 - `template.templating_style`: Styling options
@@ -604,9 +605,143 @@ For more control, you can use a nested `template` section instead of the `templa
 #### Template Variables
 
 Use the same variable insertion rules as PipeLLM:
+
 - `@variable` for block insertion (multi-line content)
 - `$variable` for inline insertion (short text)
 
+#### Construct Mode (for StructuredContent Output)
+
+PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures.
+
+**When to use construct mode:**
+
+- You need to output a structured object (not just Text)
+- You want to deterministically compose fields from existing data
+- No LLM is needed - just data composition and templating
+
+##### Basic Construct Usage
+
+```plx
+[concept.SalesSummary]
+description = "A structured sales summary"
+
+[concept.SalesSummary.structure]
+report_title = { type = "text", description = "Title of the report" }
+customer_name = { type = "text", description = "Customer name" }
+deal_value = { type = "number", description = "Deal value" }
+summary_text = { type = "text", description = "Generated summary text" }
+
+[pipe.compose_summary]
+type = "PipeCompose"
+description = "Compose a sales summary from deal data"
+inputs = { deal = "Deal" }
+output = "SalesSummary"
+
+[pipe.compose_summary.construct]
+report_title = "Monthly Sales Report"
+customer_name = { from = "deal.customer_name" }
+deal_value = { from = "deal.amount" }
+summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" }
+```
+
+##### Field Composition Methods
+
+There are four ways to define field values in a construct:
+
+**1. Fixed Value (literal)**
+
+Use a literal value directly:
+
+```plx
+[pipe.compose_report.construct]
+report_title = "Annual Report"
+report_year = 2024
+is_draft = false
+```
+
+**2. Variable Reference (`from`)**
+
+Get a value from working memory using a dotted path:
+
+```plx
+[pipe.compose_report.construct]
+customer_name = { from = "deal.customer_name" }
+total_amount = { from = "order.total" }
+street_address = { from = "customer.address.street" }
+```
+
+**3. Template (`template`)**
+
+Render a Jinja2 template with variable substitution:
+
+```plx
+[pipe.compose_report.construct]
+invoice_number = { template = "INV-$order.id" }
+summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" }
+```
+
+**4. Nested Construct**
+
+For nested structures, use a TOML subsection:
+
+```plx
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+##### Complete Construct Example
+
+```plx
+domain = "invoicing"
+
+[concept.Address]
+description = "A postal address"
+
+[concept.Address.structure]
+street = { type = "text", description = "Street address" }
+city = { type = "text", description = "City name" }
+country = { type = "text", description = "Country name" }
+
+[concept.Invoice]
+description = "An invoice document"
+
+[concept.Invoice.structure]
+invoice_number = { type = "text", description = "Invoice number" }
+total = { type = "number", description = "Total amount" }
+
+[pipe.compose_invoice]
+type = "PipeCompose"
+description = "Compose an invoice from order and customer data"
+inputs = { order = "Order", customer = "Customer" }
+output = "Invoice"
+
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+##### Key Parameters (Construct Mode)
+
+- `construct`: Dictionary mapping field names to their composition rules
+- Each field can be:
+  - A literal value (string, number, boolean)
+  - A dict with `from` key for variable reference
+  - A dict with `template` key for template rendering
+  - A nested dict for nested structures
+
+**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive.
+
 ### PipeImgGen operator
 
 The PipeImgGen operator is used to generate images using AI image generation models.
@@ -952,13 +1087,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa
         },
     )
 
-## Here we have a single input and it's a PDF.
-## Because PDFContent is a native concept, we can use it directly as a value,
+## Here we have a single input and it's a document.
+## Because DocumentContent is a native concept, we can use it directly as a value,
 ## the system knows what content it corresponds to:
     pipe_output = await execute_pipeline(
         pipe_code="power_extractor_dpe",
         inputs={
-            "document": PDFContent(url=pdf_url),
+            "document": DocumentContent(url=pdf_url),
         },
     )
 
@@ -1081,82 +1216,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart)
 ```
 
 ---
-
-## Rules to choose LLM models used in PipeLLMs.
-
-### LLM Configuration System
-
-In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset.
-LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`:
-
-- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml`
-- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml`
-- **Routing**: `.pipelex/inference/routing_profiles.toml`
-
-### LLM Handles
-
-An llm_handle can be either:
-1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system
-2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section:
-
-```toml
-[aliases]
-base-claude = "claude-4.5-sonnet"
-base-gpt = "gpt-5"
-base-gemini = "gemini-2.5-flash"
-base-mistral = "mistral-medium"
-```
-
-The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically.
-
-### Using an LLM Handle in a PipeLLM
-
-Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM:
-
-```plx
-[pipe.hello_world]
-type = "PipeLLM"
-description = "Write text about Hello World."
-output = "Text"
-model = { model = "gpt-5", temperature = 0.9 }
-prompt = """
-Write a haiku about Hello World.
-"""
-```
-
-As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto").
-
-### LLM Presets
-
-Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented.
-
-Examples:
-```toml
-llm_to_engineer = { model = "base-claude", temperature = 1 }
-llm_to_extract_invoice = { model = "claude-4.5-sonnet", temperature = 0.1, max_tokens = "auto" }
-```
-
-The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this:
-
-```plx
-[pipe.extract_invoice]
-type = "PipeLLM"
-description = "Extract invoice information from an invoice text transcript"
-inputs = { invoice_text = "InvoiceText" }
-output = "Invoice"
-model = "llm_to_extract_invoice"
-prompt = """
-Extract invoice information from this invoice:
-
-The category of this invoice is: $invoice_details.category.
-
-@invoice_text
-"""
-```
-
-The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck.
-You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets.
-
-
-You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`.
 <!-- END_PIPELEX_RULES -->
diff --git a/.cursor/rules/run_pipelex.mdc b/.cursor/rules/run_pipelex.mdc
index 8387838..7650051 100644
--- a/.cursor/rules/run_pipelex.mdc
+++ b/.cursor/rules/run_pipelex.mdc
@@ -99,13 +99,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa
         },
     )
 
-# Here we have a single input and it's a PDF.
-# Because PDFContent is a native concept, we can use it directly as a value,
+# Here we have a single input and it's a document.
+# Because DocumentContent is a native concept, we can use it directly as a value,
 # the system knows what content it corresponds to:
     pipe_output = await execute_pipeline(
         pipe_code="power_extractor_dpe",
         inputs={
-            "document": PDFContent(url=pdf_url),
+            "document": DocumentContent(url=pdf_url),
         },
     )
 
diff --git a/.cursor/rules/write_pipelex.mdc b/.cursor/rules/write_pipelex.mdc
index 9f23faf..93422cc 100644
--- a/.cursor/rules/write_pipelex.mdc
+++ b/.cursor/rules/write_pipelex.mdc
@@ -27,10 +27,10 @@ A pipeline file has three main sections:
 
 ### Domain Statement
 ```plx
-domain = "domain_name"
+domain = "domain_code"
 description = "Description of the domain" # Optional
 ```
-Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
+Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
 
 ### Concept Definitions
 
@@ -66,7 +66,7 @@ For details on how to structure concepts with fields, see the "Structuring Model
 ## Pipe Base Definition
 
 ```plx
-[pipe.your_pipe_name]
+[pipe.your_pipe_code]
 type = "PipeLLM"
 description = "A description of what your pipe does"
 inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" }
@@ -475,7 +475,7 @@ The PipeExtract operator is used to extract text and images from an image or a P
 [pipe.extract_info]
 type = "PipeExtract"
 description = "extract the information"
-inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input.
+inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input.
 output = "Page"
 ```
 
@@ -484,7 +484,7 @@ Using Extract Model Settings:
 [pipe.extract_with_model]
 type = "PipeExtract"
 description = "Extract with specific model"
-inputs = { document = "PDF" }
+inputs = { document = "Document" }
 output = "Page"
 model = "base_extract_mistral"  # Use predefined extract preset or model alias
 ```
@@ -592,15 +592,16 @@ $sales_rep.phone | $sales_rep.email
 """
 ```
 
-### Key Parameters
+### Key Parameters (Template Mode)
 
-- `template`: Inline template string (mutually exclusive with template_name)
+- `template`: Inline template string (mutually exclusive with template_name and construct)
 - `template_name`: Name of a predefined template (mutually exclusive with template)
 - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.)
 - `templating_style`: Styling options for template rendering
 - `extra_context`: Additional context variables for template
 
 For more control, you can use a nested `template` section instead of the `template` field:
+
 - `template.template`: The template string
 - `template.category`: Template type
 - `template.templating_style`: Styling options
@@ -608,9 +609,143 @@ For more control, you can use a nested `template` section instead of the `templa
 ### Template Variables
 
 Use the same variable insertion rules as PipeLLM:
+
 - `@variable` for block insertion (multi-line content)
 - `$variable` for inline insertion (short text)
 
+### Construct Mode (for StructuredContent Output)
+
+PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures.
+
+**When to use construct mode:**
+
+- You need to output a structured object (not just Text)
+- You want to deterministically compose fields from existing data
+- No LLM is needed - just data composition and templating
+
+#### Basic Construct Usage
+
+```plx
+[concept.SalesSummary]
+description = "A structured sales summary"
+
+[concept.SalesSummary.structure]
+report_title = { type = "text", description = "Title of the report" }
+customer_name = { type = "text", description = "Customer name" }
+deal_value = { type = "number", description = "Deal value" }
+summary_text = { type = "text", description = "Generated summary text" }
+
+[pipe.compose_summary]
+type = "PipeCompose"
+description = "Compose a sales summary from deal data"
+inputs = { deal = "Deal" }
+output = "SalesSummary"
+
+[pipe.compose_summary.construct]
+report_title = "Monthly Sales Report"
+customer_name = { from = "deal.customer_name" }
+deal_value = { from = "deal.amount" }
+summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" }
+```
+
+#### Field Composition Methods
+
+There are four ways to define field values in a construct:
+
+**1. Fixed Value (literal)**
+
+Use a literal value directly:
+
+```plx
+[pipe.compose_report.construct]
+report_title = "Annual Report"
+report_year = 2024
+is_draft = false
+```
+
+**2. Variable Reference (`from`)**
+
+Get a value from working memory using a dotted path:
+
+```plx
+[pipe.compose_report.construct]
+customer_name = { from = "deal.customer_name" }
+total_amount = { from = "order.total" }
+street_address = { from = "customer.address.street" }
+```
+
+**3. Template (`template`)**
+
+Render a Jinja2 template with variable substitution:
+
+```plx
+[pipe.compose_report.construct]
+invoice_number = { template = "INV-$order.id" }
+summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" }
+```
+
+**4. Nested Construct**
+
+For nested structures, use a TOML subsection:
+
+```plx
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+#### Complete Construct Example
+
+```plx
+domain = "invoicing"
+
+[concept.Address]
+description = "A postal address"
+
+[concept.Address.structure]
+street = { type = "text", description = "Street address" }
+city = { type = "text", description = "City name" }
+country = { type = "text", description = "Country name" }
+
+[concept.Invoice]
+description = "An invoice document"
+
+[concept.Invoice.structure]
+invoice_number = { type = "text", description = "Invoice number" }
+total = { type = "number", description = "Total amount" }
+
+[pipe.compose_invoice]
+type = "PipeCompose"
+description = "Compose an invoice from order and customer data"
+inputs = { order = "Order", customer = "Customer" }
+output = "Invoice"
+
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+#### Key Parameters (Construct Mode)
+
+- `construct`: Dictionary mapping field names to their composition rules
+- Each field can be:
+  - A literal value (string, number, boolean)
+  - A dict with `from` key for variable reference
+  - A dict with `template` key for template rendering
+  - A nested dict for nested structures
+
+**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive.
+
 ## PipeImgGen operator
 
 The PipeImgGen operator is used to generate images using AI image generation models.
diff --git a/.env.example b/.env.example
index 22b29d7..cbc7ad2 100644
--- a/.env.example
+++ b/.env.example
@@ -1 +1 @@
-PIPELEX_INFERENCE_API_KEY=
\ No newline at end of file
+PIPELEX_GATEWAY_API_KEY=
\ No newline at end of file
diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
index 6125caa..af4572e 100644
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -23,10 +23,10 @@ A pipeline file has three main sections:
 
 #### Domain Statement
 ```plx
-domain = "domain_name"
+domain = "domain_code"
 description = "Description of the domain" # Optional
 ```
-Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
+Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
 
 #### Concept Definitions
 
@@ -62,7 +62,7 @@ For details on how to structure concepts with fields, see the "Structuring Model
 ### Pipe Base Definition
 
 ```plx
-[pipe.your_pipe_name]
+[pipe.your_pipe_code]
 type = "PipeLLM"
 description = "A description of what your pipe does"
 inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" }
@@ -471,7 +471,7 @@ The PipeExtract operator is used to extract text and images from an image or a P
 [pipe.extract_info]
 type = "PipeExtract"
 description = "extract the information"
-inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input.
+inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input.
 output = "Page"
 ```
 
@@ -480,7 +480,7 @@ Using Extract Model Settings:
 [pipe.extract_with_model]
 type = "PipeExtract"
 description = "Extract with specific model"
-inputs = { document = "PDF" }
+inputs = { document = "Document" }
 output = "Page"
 model = "base_extract_mistral"  # Use predefined extract preset or model alias
 ```
@@ -588,15 +588,16 @@ $sales_rep.phone | $sales_rep.email
 """
 ```
 
-#### Key Parameters
+#### Key Parameters (Template Mode)
 
-- `template`: Inline template string (mutually exclusive with template_name)
+- `template`: Inline template string (mutually exclusive with template_name and construct)
 - `template_name`: Name of a predefined template (mutually exclusive with template)
 - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.)
 - `templating_style`: Styling options for template rendering
 - `extra_context`: Additional context variables for template
 
 For more control, you can use a nested `template` section instead of the `template` field:
+
 - `template.template`: The template string
 - `template.category`: Template type
 - `template.templating_style`: Styling options
@@ -604,9 +605,143 @@ For more control, you can use a nested `template` section instead of the `templa
 #### Template Variables
 
 Use the same variable insertion rules as PipeLLM:
+
 - `@variable` for block insertion (multi-line content)
 - `$variable` for inline insertion (short text)
 
+#### Construct Mode (for StructuredContent Output)
+
+PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures.
+
+**When to use construct mode:**
+
+- You need to output a structured object (not just Text)
+- You want to deterministically compose fields from existing data
+- No LLM is needed - just data composition and templating
+
+##### Basic Construct Usage
+
+```plx
+[concept.SalesSummary]
+description = "A structured sales summary"
+
+[concept.SalesSummary.structure]
+report_title = { type = "text", description = "Title of the report" }
+customer_name = { type = "text", description = "Customer name" }
+deal_value = { type = "number", description = "Deal value" }
+summary_text = { type = "text", description = "Generated summary text" }
+
+[pipe.compose_summary]
+type = "PipeCompose"
+description = "Compose a sales summary from deal data"
+inputs = { deal = "Deal" }
+output = "SalesSummary"
+
+[pipe.compose_summary.construct]
+report_title = "Monthly Sales Report"
+customer_name = { from = "deal.customer_name" }
+deal_value = { from = "deal.amount" }
+summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" }
+```
+
+##### Field Composition Methods
+
+There are four ways to define field values in a construct:
+
+**1. Fixed Value (literal)**
+
+Use a literal value directly:
+
+```plx
+[pipe.compose_report.construct]
+report_title = "Annual Report"
+report_year = 2024
+is_draft = false
+```
+
+**2. Variable Reference (`from`)**
+
+Get a value from working memory using a dotted path:
+
+```plx
+[pipe.compose_report.construct]
+customer_name = { from = "deal.customer_name" }
+total_amount = { from = "order.total" }
+street_address = { from = "customer.address.street" }
+```
+
+**3. Template (`template`)**
+
+Render a Jinja2 template with variable substitution:
+
+```plx
+[pipe.compose_report.construct]
+invoice_number = { template = "INV-$order.id" }
+summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" }
+```
+
+**4. Nested Construct**
+
+For nested structures, use a TOML subsection:
+
+```plx
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+##### Complete Construct Example
+
+```plx
+domain = "invoicing"
+
+[concept.Address]
+description = "A postal address"
+
+[concept.Address.structure]
+street = { type = "text", description = "Street address" }
+city = { type = "text", description = "City name" }
+country = { type = "text", description = "Country name" }
+
+[concept.Invoice]
+description = "An invoice document"
+
+[concept.Invoice.structure]
+invoice_number = { type = "text", description = "Invoice number" }
+total = { type = "number", description = "Total amount" }
+
+[pipe.compose_invoice]
+type = "PipeCompose"
+description = "Compose an invoice from order and customer data"
+inputs = { order = "Order", customer = "Customer" }
+output = "Invoice"
+
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+##### Key Parameters (Construct Mode)
+
+- `construct`: Dictionary mapping field names to their composition rules
+- Each field can be:
+  - A literal value (string, number, boolean)
+  - A dict with `from` key for variable reference
+  - A dict with `template` key for template rendering
+  - A nested dict for nested structures
+
+**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive.
+
 ### PipeImgGen operator
 
 The PipeImgGen operator is used to generate images using AI image generation models.
@@ -952,13 +1087,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa
         },
     )
 
-## Here we have a single input and it's a PDF.
-## Because PDFContent is a native concept, we can use it directly as a value,
+## Here we have a single input and it's a document.
+## Because DocumentContent is a native concept, we can use it directly as a value,
 ## the system knows what content it corresponds to:
     pipe_output = await execute_pipeline(
         pipe_code="power_extractor_dpe",
         inputs={
-            "document": PDFContent(url=pdf_url),
+            "document": DocumentContent(url=pdf_url),
         },
     )
 
@@ -1081,82 +1216,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart)
 ```
 
 ---
-
-## Rules to choose LLM models used in PipeLLMs.
-
-### LLM Configuration System
-
-In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset.
-LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`:
-
-- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml`
-- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml`
-- **Routing**: `.pipelex/inference/routing_profiles.toml`
-
-### LLM Handles
-
-An llm_handle can be either:
-1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system
-2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section:
-
-```toml
-[aliases]
-base-claude = "claude-4.5-sonnet"
-base-gpt = "gpt-5"
-base-gemini = "gemini-2.5-flash"
-base-mistral = "mistral-medium"
-```
-
-The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically.
-
-### Using an LLM Handle in a PipeLLM
-
-Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM:
-
-```plx
-[pipe.hello_world]
-type = "PipeLLM"
-description = "Write text about Hello World."
-output = "Text"
-model = { model = "gpt-5", temperature = 0.9 }
-prompt = """
-Write a haiku about Hello World.
-"""
-```
-
-As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto").
-
-### LLM Presets
-
-Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented.
-
-Examples:
-```toml
-llm_to_engineer = { model = "base-claude", temperature = 1 }
-llm_to_extract_invoice = { model = "claude-4.5-sonnet", temperature = 0.1, max_tokens = "auto" }
-```
-
-The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this:
-
-```plx
-[pipe.extract_invoice]
-type = "PipeLLM"
-description = "Extract invoice information from an invoice text transcript"
-inputs = { invoice_text = "InvoiceText" }
-output = "Invoice"
-model = "llm_to_extract_invoice"
-prompt = """
-Extract invoice information from this invoice:
-
-The category of this invoice is: $invoice_details.category.
-
-@invoice_text
-"""
-```
-
-The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck.
-You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets.
-
-
-You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`.
 <!-- END_PIPELEX_RULES -->
diff --git a/.github/workflows/tests-check.yml b/.github/workflows/tests-check.yml
index c94ad16..a347c29 100644
--- a/.github/workflows/tests-check.yml
+++ b/.github/workflows/tests-check.yml
@@ -43,8 +43,5 @@ jobs:
           source .venv/bin/activate
           echo -e "y\nA\n1" |pipelex init
 
-      - name: Boot test
-        run: make tp TEST=TestFundamentals
-
       - name: Run tests
         run: make gha-tests
diff --git a/.gitignore b/.gitignore
index 29670a3..443da68 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,7 +27,9 @@ dist/
 # Results
 results/
 
-# temps
 temp/
 pipelex_super.toml
+pipelex_override.toml
+telemetry_override.toml
 base_llm_deck.toml
+.pipelex/storage
diff --git a/.vscode/settings.json b/.vscode/settings.json
index e908f73..acd31dc 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -7,7 +7,6 @@
     "mypy.runUsingActiveInterpreter": true,
     "search.exclude": {
         ".mypy_cache/*": true,
-        "**/dev_context": true
     },
     "files.exclude": {
         "**/__pycache__": true,
diff --git a/.windsurfrules.md b/.windsurfrules.md
index 6125caa..af4572e 100644
--- a/.windsurfrules.md
+++ b/.windsurfrules.md
@@ -23,10 +23,10 @@ A pipeline file has three main sections:
 
 #### Domain Statement
 ```plx
-domain = "domain_name"
+domain = "domain_code"
 description = "Description of the domain" # Optional
 ```
-Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
+Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
 
 #### Concept Definitions
 
@@ -62,7 +62,7 @@ For details on how to structure concepts with fields, see the "Structuring Model
 ### Pipe Base Definition
 
 ```plx
-[pipe.your_pipe_name]
+[pipe.your_pipe_code]
 type = "PipeLLM"
 description = "A description of what your pipe does"
 inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" }
@@ -471,7 +471,7 @@ The PipeExtract operator is used to extract text and images from an image or a P
 [pipe.extract_info]
 type = "PipeExtract"
 description = "extract the information"
-inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input.
+inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input.
 output = "Page"
 ```
 
@@ -480,7 +480,7 @@ Using Extract Model Settings:
 [pipe.extract_with_model]
 type = "PipeExtract"
 description = "Extract with specific model"
-inputs = { document = "PDF" }
+inputs = { document = "Document" }
 output = "Page"
 model = "base_extract_mistral"  # Use predefined extract preset or model alias
 ```
@@ -588,15 +588,16 @@ $sales_rep.phone | $sales_rep.email
 """
 ```
 
-#### Key Parameters
+#### Key Parameters (Template Mode)
 
-- `template`: Inline template string (mutually exclusive with template_name)
+- `template`: Inline template string (mutually exclusive with template_name and construct)
 - `template_name`: Name of a predefined template (mutually exclusive with template)
 - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.)
 - `templating_style`: Styling options for template rendering
 - `extra_context`: Additional context variables for template
 
 For more control, you can use a nested `template` section instead of the `template` field:
+
 - `template.template`: The template string
 - `template.category`: Template type
 - `template.templating_style`: Styling options
@@ -604,9 +605,143 @@ For more control, you can use a nested `template` section instead of the `templa
 #### Template Variables
 
 Use the same variable insertion rules as PipeLLM:
+
 - `@variable` for block insertion (multi-line content)
 - `$variable` for inline insertion (short text)
 
+#### Construct Mode (for StructuredContent Output)
+
+PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures.
+
+**When to use construct mode:**
+
+- You need to output a structured object (not just Text)
+- You want to deterministically compose fields from existing data
+- No LLM is needed - just data composition and templating
+
+##### Basic Construct Usage
+
+```plx
+[concept.SalesSummary]
+description = "A structured sales summary"
+
+[concept.SalesSummary.structure]
+report_title = { type = "text", description = "Title of the report" }
+customer_name = { type = "text", description = "Customer name" }
+deal_value = { type = "number", description = "Deal value" }
+summary_text = { type = "text", description = "Generated summary text" }
+
+[pipe.compose_summary]
+type = "PipeCompose"
+description = "Compose a sales summary from deal data"
+inputs = { deal = "Deal" }
+output = "SalesSummary"
+
+[pipe.compose_summary.construct]
+report_title = "Monthly Sales Report"
+customer_name = { from = "deal.customer_name" }
+deal_value = { from = "deal.amount" }
+summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" }
+```
+
+##### Field Composition Methods
+
+There are four ways to define field values in a construct:
+
+**1. Fixed Value (literal)**
+
+Use a literal value directly:
+
+```plx
+[pipe.compose_report.construct]
+report_title = "Annual Report"
+report_year = 2024
+is_draft = false
+```
+
+**2. Variable Reference (`from`)**
+
+Get a value from working memory using a dotted path:
+
+```plx
+[pipe.compose_report.construct]
+customer_name = { from = "deal.customer_name" }
+total_amount = { from = "order.total" }
+street_address = { from = "customer.address.street" }
+```
+
+**3. Template (`template`)**
+
+Render a Jinja2 template with variable substitution:
+
+```plx
+[pipe.compose_report.construct]
+invoice_number = { template = "INV-$order.id" }
+summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" }
+```
+
+**4. Nested Construct**
+
+For nested structures, use a TOML subsection:
+
+```plx
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+##### Complete Construct Example
+
+```plx
+domain = "invoicing"
+
+[concept.Address]
+description = "A postal address"
+
+[concept.Address.structure]
+street = { type = "text", description = "Street address" }
+city = { type = "text", description = "City name" }
+country = { type = "text", description = "Country name" }
+
+[concept.Invoice]
+description = "An invoice document"
+
+[concept.Invoice.structure]
+invoice_number = { type = "text", description = "Invoice number" }
+total = { type = "number", description = "Total amount" }
+
+[pipe.compose_invoice]
+type = "PipeCompose"
+description = "Compose an invoice from order and customer data"
+inputs = { order = "Order", customer = "Customer" }
+output = "Invoice"
+
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+##### Key Parameters (Construct Mode)
+
+- `construct`: Dictionary mapping field names to their composition rules
+- Each field can be:
+  - A literal value (string, number, boolean)
+  - A dict with `from` key for variable reference
+  - A dict with `template` key for template rendering
+  - A nested dict for nested structures
+
+**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive.
+
 ### PipeImgGen operator
 
 The PipeImgGen operator is used to generate images using AI image generation models.
@@ -952,13 +1087,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa
         },
     )
 
-## Here we have a single input and it's a PDF.
-## Because PDFContent is a native concept, we can use it directly as a value,
+## Here we have a single input and it's a document.
+## Because DocumentContent is a native concept, we can use it directly as a value,
 ## the system knows what content it corresponds to:
     pipe_output = await execute_pipeline(
         pipe_code="power_extractor_dpe",
         inputs={
-            "document": PDFContent(url=pdf_url),
+            "document": DocumentContent(url=pdf_url),
         },
     )
 
@@ -1081,82 +1216,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart)
 ```
 
 ---
-
-## Rules to choose LLM models used in PipeLLMs.
-
-### LLM Configuration System
-
-In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset.
-LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`:
-
-- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml`
-- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml`
-- **Routing**: `.pipelex/inference/routing_profiles.toml`
-
-### LLM Handles
-
-An llm_handle can be either:
-1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system
-2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section:
-
-```toml
-[aliases]
-base-claude = "claude-4.5-sonnet"
-base-gpt = "gpt-5"
-base-gemini = "gemini-2.5-flash"
-base-mistral = "mistral-medium"
-```
-
-The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically.
-
-### Using an LLM Handle in a PipeLLM
-
-Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM:
-
-```plx
-[pipe.hello_world]
-type = "PipeLLM"
-description = "Write text about Hello World."
-output = "Text"
-model = { model = "gpt-5", temperature = 0.9 }
-prompt = """
-Write a haiku about Hello World.
-"""
-```
-
-As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto").
-
-### LLM Presets
-
-Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented.
-
-Examples:
-```toml
-llm_to_engineer = { model = "base-claude", temperature = 1 }
-llm_to_extract_invoice = { model = "claude-4.5-sonnet", temperature = 0.1, max_tokens = "auto" }
-```
-
-The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this:
-
-```plx
-[pipe.extract_invoice]
-type = "PipeLLM"
-description = "Extract invoice information from an invoice text transcript"
-inputs = { invoice_text = "InvoiceText" }
-output = "Invoice"
-model = "llm_to_extract_invoice"
-prompt = """
-Extract invoice information from this invoice:
-
-The category of this invoice is: $invoice_details.category.
-
-@invoice_text
-"""
-```
-
-The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck.
-You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets.
-
-
-You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`.
 <!-- END_PIPELEX_RULES -->
diff --git a/AGENTS.md b/AGENTS.md
index 6125caa..af4572e 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -23,10 +23,10 @@ A pipeline file has three main sections:
 
 #### Domain Statement
 ```plx
-domain = "domain_name"
+domain = "domain_code"
 description = "Description of the domain" # Optional
 ```
-Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
+Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
 
 #### Concept Definitions
 
@@ -62,7 +62,7 @@ For details on how to structure concepts with fields, see the "Structuring Model
 ### Pipe Base Definition
 
 ```plx
-[pipe.your_pipe_name]
+[pipe.your_pipe_code]
 type = "PipeLLM"
 description = "A description of what your pipe does"
 inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" }
@@ -471,7 +471,7 @@ The PipeExtract operator is used to extract text and images from an image or a P
 [pipe.extract_info]
 type = "PipeExtract"
 description = "extract the information"
-inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input.
+inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input.
 output = "Page"
 ```
 
@@ -480,7 +480,7 @@ Using Extract Model Settings:
 [pipe.extract_with_model]
 type = "PipeExtract"
 description = "Extract with specific model"
-inputs = { document = "PDF" }
+inputs = { document = "Document" }
 output = "Page"
 model = "base_extract_mistral"  # Use predefined extract preset or model alias
 ```
@@ -588,15 +588,16 @@ $sales_rep.phone | $sales_rep.email
 """
 ```
 
-#### Key Parameters
+#### Key Parameters (Template Mode)
 
-- `template`: Inline template string (mutually exclusive with template_name)
+- `template`: Inline template string (mutually exclusive with template_name and construct)
 - `template_name`: Name of a predefined template (mutually exclusive with template)
 - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.)
 - `templating_style`: Styling options for template rendering
 - `extra_context`: Additional context variables for template
 
 For more control, you can use a nested `template` section instead of the `template` field:
+
 - `template.template`: The template string
 - `template.category`: Template type
 - `template.templating_style`: Styling options
@@ -604,9 +605,143 @@ For more control, you can use a nested `template` section instead of the `templa
 #### Template Variables
 
 Use the same variable insertion rules as PipeLLM:
+
 - `@variable` for block insertion (multi-line content)
 - `$variable` for inline insertion (short text)
 
+#### Construct Mode (for StructuredContent Output)
+
+PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures.
+
+**When to use construct mode:**
+
+- You need to output a structured object (not just Text)
+- You want to deterministically compose fields from existing data
+- No LLM is needed - just data composition and templating
+
+##### Basic Construct Usage
+
+```plx
+[concept.SalesSummary]
+description = "A structured sales summary"
+
+[concept.SalesSummary.structure]
+report_title = { type = "text", description = "Title of the report" }
+customer_name = { type = "text", description = "Customer name" }
+deal_value = { type = "number", description = "Deal value" }
+summary_text = { type = "text", description = "Generated summary text" }
+
+[pipe.compose_summary]
+type = "PipeCompose"
+description = "Compose a sales summary from deal data"
+inputs = { deal = "Deal" }
+output = "SalesSummary"
+
+[pipe.compose_summary.construct]
+report_title = "Monthly Sales Report"
+customer_name = { from = "deal.customer_name" }
+deal_value = { from = "deal.amount" }
+summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" }
+```
+
+##### Field Composition Methods
+
+There are four ways to define field values in a construct:
+
+**1. Fixed Value (literal)**
+
+Use a literal value directly:
+
+```plx
+[pipe.compose_report.construct]
+report_title = "Annual Report"
+report_year = 2024
+is_draft = false
+```
+
+**2. Variable Reference (`from`)**
+
+Get a value from working memory using a dotted path:
+
+```plx
+[pipe.compose_report.construct]
+customer_name = { from = "deal.customer_name" }
+total_amount = { from = "order.total" }
+street_address = { from = "customer.address.street" }
+```
+
+**3. Template (`template`)**
+
+Render a Jinja2 template with variable substitution:
+
+```plx
+[pipe.compose_report.construct]
+invoice_number = { template = "INV-$order.id" }
+summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" }
+```
+
+**4. Nested Construct**
+
+For nested structures, use a TOML subsection:
+
+```plx
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+##### Complete Construct Example
+
+```plx
+domain = "invoicing"
+
+[concept.Address]
+description = "A postal address"
+
+[concept.Address.structure]
+street = { type = "text", description = "Street address" }
+city = { type = "text", description = "City name" }
+country = { type = "text", description = "Country name" }
+
+[concept.Invoice]
+description = "An invoice document"
+
+[concept.Invoice.structure]
+invoice_number = { type = "text", description = "Invoice number" }
+total = { type = "number", description = "Total amount" }
+
+[pipe.compose_invoice]
+type = "PipeCompose"
+description = "Compose an invoice from order and customer data"
+inputs = { order = "Order", customer = "Customer" }
+output = "Invoice"
+
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+##### Key Parameters (Construct Mode)
+
+- `construct`: Dictionary mapping field names to their composition rules
+- Each field can be:
+  - A literal value (string, number, boolean)
+  - A dict with `from` key for variable reference
+  - A dict with `template` key for template rendering
+  - A nested dict for nested structures
+
+**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive.
+
 ### PipeImgGen operator
 
 The PipeImgGen operator is used to generate images using AI image generation models.
@@ -952,13 +1087,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa
         },
     )
 
-## Here we have a single input and it's a PDF.
-## Because PDFContent is a native concept, we can use it directly as a value,
+## Here we have a single input and it's a document.
+## Because DocumentContent is a native concept, we can use it directly as a value,
 ## the system knows what content it corresponds to:
     pipe_output = await execute_pipeline(
         pipe_code="power_extractor_dpe",
         inputs={
-            "document": PDFContent(url=pdf_url),
+            "document": DocumentContent(url=pdf_url),
         },
     )
 
@@ -1081,82 +1216,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart)
 ```
 
 ---
-
-## Rules to choose LLM models used in PipeLLMs.
-
-### LLM Configuration System
-
-In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset.
-LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`:
-
-- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml`
-- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml`
-- **Routing**: `.pipelex/inference/routing_profiles.toml`
-
-### LLM Handles
-
-An llm_handle can be either:
-1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system
-2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section:
-
-```toml
-[aliases]
-base-claude = "claude-4.5-sonnet"
-base-gpt = "gpt-5"
-base-gemini = "gemini-2.5-flash"
-base-mistral = "mistral-medium"
-```
-
-The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically.
-
-### Using an LLM Handle in a PipeLLM
-
-Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM:
-
-```plx
-[pipe.hello_world]
-type = "PipeLLM"
-description = "Write text about Hello World."
-output = "Text"
-model = { model = "gpt-5", temperature = 0.9 }
-prompt = """
-Write a haiku about Hello World.
-"""
-```
-
-As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto").
-
-### LLM Presets
-
-Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented.
-
-Examples:
-```toml
-llm_to_engineer = { model = "base-claude", temperature = 1 }
-llm_to_extract_invoice = { model = "claude-4.5-sonnet", temperature = 0.1, max_tokens = "auto" }
-```
-
-The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this:
-
-```plx
-[pipe.extract_invoice]
-type = "PipeLLM"
-description = "Extract invoice information from an invoice text transcript"
-inputs = { invoice_text = "InvoiceText" }
-output = "Invoice"
-model = "llm_to_extract_invoice"
-prompt = """
-Extract invoice information from this invoice:
-
-The category of this invoice is: $invoice_details.category.
-
-@invoice_text
-"""
-```
-
-The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck.
-You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets.
-
-
-You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`.
 <!-- END_PIPELEX_RULES -->
diff --git a/CLAUDE.md b/CLAUDE.md
index 6125caa..af4572e 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -23,10 +23,10 @@ A pipeline file has three main sections:
 
 #### Domain Statement
 ```plx
-domain = "domain_name"
+domain = "domain_code"
 description = "Description of the domain" # Optional
 ```
-Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
+Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name.
 
 #### Concept Definitions
 
@@ -62,7 +62,7 @@ For details on how to structure concepts with fields, see the "Structuring Model
 ### Pipe Base Definition
 
 ```plx
-[pipe.your_pipe_name]
+[pipe.your_pipe_code]
 type = "PipeLLM"
 description = "A description of what your pipe does"
 inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" }
@@ -471,7 +471,7 @@ The PipeExtract operator is used to extract text and images from an image or a P
 [pipe.extract_info]
 type = "PipeExtract"
 description = "extract the information"
-inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input.
+inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input.
 output = "Page"
 ```
 
@@ -480,7 +480,7 @@ Using Extract Model Settings:
 [pipe.extract_with_model]
 type = "PipeExtract"
 description = "Extract with specific model"
-inputs = { document = "PDF" }
+inputs = { document = "Document" }
 output = "Page"
 model = "base_extract_mistral"  # Use predefined extract preset or model alias
 ```
@@ -588,15 +588,16 @@ $sales_rep.phone | $sales_rep.email
 """
 ```
 
-#### Key Parameters
+#### Key Parameters (Template Mode)
 
-- `template`: Inline template string (mutually exclusive with template_name)
+- `template`: Inline template string (mutually exclusive with template_name and construct)
 - `template_name`: Name of a predefined template (mutually exclusive with template)
 - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.)
 - `templating_style`: Styling options for template rendering
 - `extra_context`: Additional context variables for template
 
 For more control, you can use a nested `template` section instead of the `template` field:
+
 - `template.template`: The template string
 - `template.category`: Template type
 - `template.templating_style`: Styling options
@@ -604,9 +605,143 @@ For more control, you can use a nested `template` section instead of the `templa
 #### Template Variables
 
 Use the same variable insertion rules as PipeLLM:
+
 - `@variable` for block insertion (multi-line content)
 - `$variable` for inline insertion (short text)
 
+#### Construct Mode (for StructuredContent Output)
+
+PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures.
+
+**When to use construct mode:**
+
+- You need to output a structured object (not just Text)
+- You want to deterministically compose fields from existing data
+- No LLM is needed - just data composition and templating
+
+##### Basic Construct Usage
+
+```plx
+[concept.SalesSummary]
+description = "A structured sales summary"
+
+[concept.SalesSummary.structure]
+report_title = { type = "text", description = "Title of the report" }
+customer_name = { type = "text", description = "Customer name" }
+deal_value = { type = "number", description = "Deal value" }
+summary_text = { type = "text", description = "Generated summary text" }
+
+[pipe.compose_summary]
+type = "PipeCompose"
+description = "Compose a sales summary from deal data"
+inputs = { deal = "Deal" }
+output = "SalesSummary"
+
+[pipe.compose_summary.construct]
+report_title = "Monthly Sales Report"
+customer_name = { from = "deal.customer_name" }
+deal_value = { from = "deal.amount" }
+summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" }
+```
+
+##### Field Composition Methods
+
+There are four ways to define field values in a construct:
+
+**1. Fixed Value (literal)**
+
+Use a literal value directly:
+
+```plx
+[pipe.compose_report.construct]
+report_title = "Annual Report"
+report_year = 2024
+is_draft = false
+```
+
+**2. Variable Reference (`from`)**
+
+Get a value from working memory using a dotted path:
+
+```plx
+[pipe.compose_report.construct]
+customer_name = { from = "deal.customer_name" }
+total_amount = { from = "order.total" }
+street_address = { from = "customer.address.street" }
+```
+
+**3. Template (`template`)**
+
+Render a Jinja2 template with variable substitution:
+
+```plx
+[pipe.compose_report.construct]
+invoice_number = { template = "INV-$order.id" }
+summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" }
+```
+
+**4. Nested Construct**
+
+For nested structures, use a TOML subsection:
+
+```plx
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+##### Complete Construct Example
+
+```plx
+domain = "invoicing"
+
+[concept.Address]
+description = "A postal address"
+
+[concept.Address.structure]
+street = { type = "text", description = "Street address" }
+city = { type = "text", description = "City name" }
+country = { type = "text", description = "Country name" }
+
+[concept.Invoice]
+description = "An invoice document"
+
+[concept.Invoice.structure]
+invoice_number = { type = "text", description = "Invoice number" }
+total = { type = "number", description = "Total amount" }
+
+[pipe.compose_invoice]
+type = "PipeCompose"
+description = "Compose an invoice from order and customer data"
+inputs = { order = "Order", customer = "Customer" }
+output = "Invoice"
+
+[pipe.compose_invoice.construct]
+invoice_number = { template = "INV-$order.id" }
+total = { from = "order.total_amount" }
+
+[pipe.compose_invoice.construct.billing_address]
+street = { from = "customer.address.street" }
+city = { from = "customer.address.city" }
+country = "France"
+```
+
+##### Key Parameters (Construct Mode)
+
+- `construct`: Dictionary mapping field names to their composition rules
+- Each field can be:
+  - A literal value (string, number, boolean)
+  - A dict with `from` key for variable reference
+  - A dict with `template` key for template rendering
+  - A nested dict for nested structures
+
+**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive.
+
 ### PipeImgGen operator
 
 The PipeImgGen operator is used to generate images using AI image generation models.
@@ -952,13 +1087,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa
         },
     )
 
-## Here we have a single input and it's a PDF.
-## Because PDFContent is a native concept, we can use it directly as a value,
+## Here we have a single input and it's a document.
+## Because DocumentContent is a native concept, we can use it directly as a value,
 ## the system knows what content it corresponds to:
     pipe_output = await execute_pipeline(
         pipe_code="power_extractor_dpe",
         inputs={
-            "document": PDFContent(url=pdf_url),
+            "document": DocumentContent(url=pdf_url),
         },
     )
 
@@ -1081,82 +1216,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart)
 ```
 
 ---
-
-## Rules to choose LLM models used in PipeLLMs.
-
-### LLM Configuration System
-
-In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset.
-LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`:
-
-- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml`
-- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml`
-- **Routing**: `.pipelex/inference/routing_profiles.toml`
-
-### LLM Handles
-
-An llm_handle can be either:
-1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system
-2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section:
-
-```toml
-[aliases]
-base-claude = "claude-4.5-sonnet"
-base-gpt = "gpt-5"
-base-gemini = "gemini-2.5-flash"
-base-mistral = "mistral-medium"
-```
-
-The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically.
-
-### Using an LLM Handle in a PipeLLM
-
-Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM:
-
-```plx
-[pipe.hello_world]
-type = "PipeLLM"
-description = "Write text about Hello World."
-output = "Text"
-model = { model = "gpt-5", temperature = 0.9 }
-prompt = """
-Write a haiku about Hello World.
-"""
-```
-
-As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto").
-
-### LLM Presets
-
-Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented.
-
-Examples:
-```toml
-llm_to_engineer = { model = "base-claude", temperature = 1 }
-llm_to_extract_invoice = { model = "claude-4.5-sonnet", temperature = 0.1, max_tokens = "auto" }
-```
-
-The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this:
-
-```plx
-[pipe.extract_invoice]
-type = "PipeLLM"
-description = "Extract invoice information from an invoice text transcript"
-inputs = { invoice_text = "InvoiceText" }
-output = "Invoice"
-model = "llm_to_extract_invoice"
-prompt = """
-Extract invoice information from this invoice:
-
-The category of this invoice is: $invoice_details.category.
-
-@invoice_text
-"""
-```
-
-The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck.
-You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets.
-
-
-You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`.
 <!-- END_PIPELEX_RULES -->
diff --git a/Makefile b/Makefile
index 803118d..bb1c61f 100644
--- a/Makefile
+++ b/Makefile
@@ -6,7 +6,7 @@ VIRTUAL_ENV := $(CURDIR)/.venv
 PROJECT_NAME := $(shell grep '^name = ' pyproject.toml | sed -E 's/name = "(.*)"/\1/')
 
 # The "?" is used to make the variable optional, so that it can be overridden by the user.
-PYTHON_VERSION ?= 3.11
+PYTHON_VERSION ?= 3.13
 VENV_PYTHON := $(VIRTUAL_ENV)/bin/python
 VENV_PYTEST := $(VIRTUAL_ENV)/bin/pytest
 VENV_RUFF := $(VIRTUAL_ENV)/bin/ruff
@@ -16,7 +16,7 @@ VENV_PIPELEX := $(VIRTUAL_ENV)/bin/pipelex
 
 UV_MIN_VERSION = $(shell grep -m1 'required-version' pyproject.toml | sed -E 's/.*= *"([^<>=, ]+).*/\1/')
 
-USUAL_PYTEST_MARKERS := "(dry_runnable or not (inference or llm or imgg or ocr)) and not (needs_output or pipelex_api)"
+USUAL_PYTEST_MARKERS := "(dry_runnable or not inference) and not (needs_output or pipelex_api)"
 
 define PRINT_TITLE
     $(eval PROJECT_PART := [$(PROJECT_NAME)])
@@ -117,7 +117,7 @@ env: check-uv
 	$(call PRINT_TITLE,"Creating virtual environment")
 	@if [ ! -d $(VIRTUAL_ENV) ]; then \
 		echo "Creating Python virtual env in \`${VIRTUAL_ENV}\`"; \
-		uv venv $(VIRTUAL_ENV) --python 3.11; \
+		uv venv $(VIRTUAL_ENV) --python $(PYTHON_VERSION); \
 	else \
 		echo "Python virtual env already exists in \`${VIRTUAL_ENV}\`"; \
 	fi
@@ -209,12 +209,12 @@ cleanall: cleanderived cleanenv cleanconfig
 codex-tests: env
 	$(call PRINT_TITLE,"Unit testing for Codex")
 	@echo "• Running unit tests for Codex (excluding inference and codex_disabled)"
-	$(VENV_PYTEST) --exitfirst --quiet -m "not inference and not codex_disabled" || [ $$? = 5 ]
+	$(VENV_PYTEST) --disable-inference --exitfirst --quiet -m "not inference and not codex_disabled" || [ $$? = 5 ]
 
 gha-tests: env
 	$(call PRINT_TITLE,"Unit testing for github actions")
 	@echo "• Running unit tests for github actions (excluding inference and gha_disabled)"
-	$(VENV_PYTEST) --exitfirst --quiet -m "not inference and not gha_disabled" || [ $$? = 5 ]
+	$(VENV_PYTEST) --disable-inference --exitfirst --quiet -m "not inference and not gha_disabled" || [ $$? = 5 ]
 
 run-all-tests: env
 	$(call PRINT_TITLE,"Running all unit tests")
diff --git a/my_project/hello_world.py b/my_project/hello_world.py
index 5614816..f70cd9e 100644
--- a/my_project/hello_world.py
+++ b/my_project/hello_world.py
@@ -17,8 +17,11 @@ async def hello_world():
     # Print the output
     pretty_print(pipe_output, title="Your first Pipelex output")
 
+    # get the generated text
+    generated_text = pipe_output.main_stuff_as_str
+    pretty_print(generated_text, title="Generated text")
+
 
 # start Pipelex
-Pipelex.make()
-# run sample using asyncio
-asyncio.run(hello_world())
+with Pipelex.make(library_dirs=["my_project"]):
+    asyncio.run(hello_world())
diff --git a/pyproject.toml b/pyproject.toml
index 1cfd5d7..fbb9661 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,19 +5,22 @@ description = "Replace this with your project description"
 # authors = [{ name = "Your Name", email = "your.email@example.com" }]
 license = "MIT"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.10,<3.15"
 classifiers = [
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
     "Operating System :: OS Independent",
 ]
 
-dependencies = [
-    "pipelex[mistralai,anthropic,google,google-genai,bedrock,fal]==0.17.3",
-]
+dependencies = ["pipelex[mistralai,anthropic,google,google-genai,bedrock,fal]"]
+
+[tool.uv.sources]
+pipelex = { git = "https://github.com/Pipelex/pipelex.git", branch = "pre-release/v0.18.0b3" }
+
 
 [tool.setuptools]
 packages = ["my_project"]
diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py
index 028da5f..ab98a37 100644
--- a/tests/integration/conftest.py
+++ b/tests/integration/conftest.py
@@ -2,6 +2,8 @@
 import pipelex.pipelex
 import pytest
 from pipelex.system.configuration.config_check import check_is_initialized
+from pipelex.test_extras.shared_pytest_plugins import is_inference_disabled_in_pipelex
+from pytest import FixtureRequest
 from rich import print
 from rich.console import Console
 from rich.traceback import Traceback
@@ -15,11 +17,13 @@ def check_pipelex_initialized():
 
 
 @pytest.fixture(scope="module", autouse=True)
-def reset_pipelex_config_fixture():
+def reset_pipelex_config_fixture(request: FixtureRequest):
     # Code to run before each test
     print("\n[magenta]pipelex setup[/magenta]")
     try:
-        pipelex_instance = pipelex.pipelex.Pipelex.make()
+        pipelex_instance = pipelex.pipelex.Pipelex.make(
+            disable_inference=is_inference_disabled_in_pipelex(request),
+        )
     except Exception as exc:
         Console().print(Traceback())
         pytest.exit(f"Critical Pipelex setup error: {exc}")
diff --git a/uv.lock b/uv.lock
index f5923b6..44c8eb5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,6 +1,6 @@
 version = 1
 revision = 3
-requires-python = ">=3.10"
+requires-python = ">=3.10, <3.15"
 resolution-markers = [
     "python_full_version >= '3.11'",
     "python_full_version < '3.11'",
@@ -213,7 +213,7 @@ wheels = [
 
 [[package]]
 name = "anthropic"
-version = "0.75.0"
+version = "0.79.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -225,9 +225,9 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/04/1f/08e95f4b7e2d35205ae5dcbb4ae97e7d477fc521c275c02609e2931ece2d/anthropic-0.75.0.tar.gz", hash = "sha256:e8607422f4ab616db2ea5baacc215dd5f028da99ce2f022e33c7c535b29f3dfb", size = 439565, upload-time = "2025-11-24T20:41:45.28Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/15/b1/91aea3f8fd180d01d133d931a167a78a3737b3fd39ccef2ae8d6619c24fd/anthropic-0.79.0.tar.gz", hash = "sha256:8707aafb3b1176ed6c13e2b1c9fb3efddce90d17aee5d8b83a86c70dcdcca871", size = 509825, upload-time = "2026-02-07T18:06:18.388Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/60/1c/1cd02b7ae64302a6e06724bf80a96401d5313708651d277b1458504a1730/anthropic-0.75.0-py3-none-any.whl", hash = "sha256:ea8317271b6c15d80225a9f3c670152746e88805a7a61e14d4a374577164965b", size = 388164, upload-time = "2025-11-24T20:41:43.587Z" },
+    { url = "https://files.pythonhosted.org/packages/95/b2/cc0b8e874a18d7da50b0fda8c99e4ac123f23bf47b471827c5f6f3e4a767/anthropic-0.79.0-py3-none-any.whl", hash = "sha256:04cbd473b6bbda4ca2e41dd670fe2f829a911530f01697d0a1e37321eb75f3cf", size = 405918, upload-time = "2026-02-07T18:06:20.246Z" },
 ]
 
 [[package]]
@@ -343,13 +343,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/68/b6/f624f5143bc5f7a66b79fc40e67b30b9584471a6143062af420bc83ae887/botocore_stubs-1.41.6-py3-none-any.whl", hash = "sha256:859e4147b5b14dc5eb64fc84fa02424839354368a0fea41da52c7a1d06427e37", size = 66748, upload-time = "2025-12-01T04:14:12.833Z" },
 ]
 
+[[package]]
+name = "cached-property"
+version = "2.0.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/76/4b/3d870836119dbe9a5e3c9a61af8cc1a8b69d75aea564572e385882d5aefb/cached_property-2.0.1.tar.gz", hash = "sha256:484d617105e3ee0e4f1f58725e72a8ef9e93deee462222dbd51cd91230897641", size = 10574, upload-time = "2024-10-25T15:43:55.667Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/0e/7d8225aab3bc1a0f5811f8e1b557aa034ac04bdf641925b30d3caf586b28/cached_property-2.0.1-py3-none-any.whl", hash = "sha256:f617d70ab1100b7bcf6e42228f9ddcb78c676ffa167278d9f730d1c2fba69ccb", size = 7428, upload-time = "2024-10-25T15:43:54.711Z" },
+]
+
 [[package]]
 name = "cachetools"
-version = "6.2.2"
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